基于人工智能方法的气动驱动系统的状态监测 送-向前-向后传播 神经网络
1Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123 Brescia, Italy.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究证明了使用神经网络的气动系统可靠的机器状态监控. 与Arduino板集成的低成本振动传感器为工业设备维护提供了可行的替代方案.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 机器状态监测对于高效的工业设备维护至关重要.
- 气动系统被广泛使用,但需要有效的监控策略.
- 传统的监测可能是昂贵的,限制了采用.
研究的目的:
- 为了评估一个前向反向传播神经网络的有效性,用于气动系统状态监测.
- 用不同的传感器信号和提取的特征来比较分类性能.
- 评估使用低成本传感器进行可靠监控的可行性.
主要方法:
- 使用工业和低成本 (Arduino) 传感器获取气动气振动数据.
- 压力和位置传感器数据的集成.
- 使用功率光谱密度 (PSD) 和统计指数从加速信号中提取特征.
- 从使用统计指数的压力和位置传感器中提取特征.
- 使用前神经网络对操作状态的分类.
主要成果:
- 送神经网络成功识别了具有高可靠性的运行状态.
- 振动数据,即使是来自低成本传感器,也可以实现可靠的状态监测.
- 不同的传感器输入和提取的特征显示了不同的分类性能.
结论:
- 基于神经网络的分类是气动系统状态监测的可靠方法.
- 低成本的仪器仪表,特别是振动传感器,可以促进有效的状态监测.
- 这种方法可以显著增加在工业中采用先进的维护策略.
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